{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ЭТО СИНТЕТИКА"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import csv\n",
    "import wfdb\n",
    "import heartpy as hp\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.signal as ssignal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cheby(ppg, fs, order=4, rs=20, btype='low'):\n",
    "    nyquist = 0.5 * fs\n",
    "    cutoff = 10 / nyquist\n",
    "    rs, order = 20, 4\n",
    "    b, a = ssignal.cheby2(N=order, rs=rs, Wn=cutoff, btype='low')\n",
    "    return ssignal.filtfilt(b, a, ppg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "988\n",
      "500\n",
      "{'bpm': nan, 'ibi': nan, 'sdnn': nan, 'sdsd': masked, 'rmssd': nan, 'pnn20': nan, 'pnn50': nan, 'hr_mad': nan, 'sd1': nan, 'sd2': nan, 's': nan, 'sd1/sd2': nan, 'breathingrate': nan}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\toha2\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "c:\\Users\\toha2\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\core\\_methods.py:269: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\toha2\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\ma\\core.py:5317: RuntimeWarning: Mean of empty slice.\n",
      "  result = super().mean(axis=axis, dtype=dtype, **kwargs)[()]\n",
      "c:\\Users\\toha2\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n"
     ]
    }
   ],
   "source": [
    "record = wfdb.rdrecord(r\"D:\\Proga\\AML\\datasets\\PWDB\\PWs_wfdb\\wfdb\\pwdb0003\")\n",
    "#print(record.__dict__)\n",
    "cindex = record.sig_name.index('Radial_PPG,')\n",
    "signal = record.p_signal[:cindex] + record.p_signal[:cindex] + record.p_signal[:cindex]\n",
    "\n",
    "print(len(signal.flatten()))\n",
    "print(record.fs)\n",
    "\n",
    "signal = cheby(signal.flatten(),fs=record.fs, order=4)\n",
    "\n",
    "wd,m = hp.process(signal,sample_rate=record.fs)\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Длина сигнала: 386\n",
      "Частота дискретизации: 500 Гц\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bpm': nan, 'ibi': nan, 'sdnn': nan, 'sdsd': masked, 'rmssd': nan, 'pnn20': nan, 'pnn50': nan, 'hr_mad': nan, 'sd1': nan, 'sd2': nan, 's': nan, 'sd1/sd2': nan, 'breathingrate': nan}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\toha2\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "c:\\Users\\toha2\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\core\\_methods.py:269: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
      "c:\\Users\\toha2\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\ma\\core.py:5317: RuntimeWarning: Mean of empty slice.\n",
      "  result = super().mean(axis=axis, dtype=dtype, **kwargs)[()]\n",
      "c:\\Users\\toha2\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n"
     ]
    }
   ],
   "source": [
    "import wfdb\n",
    "import heartpy as hp\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Чтение записи\n",
    "record = wfdb.rdrecord(r\"D:\\Proga\\AML\\datasets\\PWDB\\PWs_wfdb\\wfdb\\pwdb0003\")\n",
    "\n",
    "# Нахождение индекса сигнала Radial_PPG\n",
    "cindex = record.sig_name.index('Radial_PPG,')\n",
    "signal = record.p_signal[:, cindex].flatten()\n",
    "\n",
    "# Вывод информации о сигнале\n",
    "print(f\"Длина сигнала: {len(signal)}\")\n",
    "print(f\"Частота дискретизации: {record.fs} Гц\")\n",
    "\n",
    "# Фильтрация сигнала\n",
    "def funcBPFilter(signal, order=2, f1=0.5, f2=4.0, fs=record.fs):\n",
    "    from scipy.signal import butter, lfilter\n",
    "    nyq = 0.5 * fs\n",
    "    low = f1 / nyq\n",
    "    high = f2 / nyq\n",
    "    b, a = butter(order, [low, high], btype='band')\n",
    "    y = lfilter(b, a, signal)\n",
    "    return y\n",
    "\n",
    "signal_filtered = funcBPFilter(signal, order=2, f1=0.5, f2=4.0, fs=record.fs)\n",
    "\n",
    "# Визуализация сигнала до и после фильтрации\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(signal)\n",
    "plt.title('Сырой сигнал')\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(signal_filtered)\n",
    "plt.title('Отфильтрованный сигнал')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Обработка сигнала с помощью heartpy\n",
    "try:\n",
    "    wd, m = hp.process(signal_filtered, sample_rate=record.fs)\n",
    "    print(m)\n",
    "except hp.exceptions.BadSignalWarning as e:\n",
    "    print(e)"
   ]
  }
 ],
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   "file_extension": ".py",
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